33 research outputs found
Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review
Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures. However, obtaining reliable and accurate medical image annotations often necessitates expert guidance, making the acquisition of large-scale annotated datasets challenging and costly. This presents obstacles for traditional supervised learning methods. Consequently, semi-supervised learning (SSL) has emerged as a promising solution, capable of utilizing unlabeled data to enhance model performance and has been widely adopted in medical image segmentation tasks. However, striking a balance between segmentation accuracy and inference speed remains a challenge for real-time segmentation. This paper provides a comprehensive review of research progress in real-time intelligent semi-supervised ultrasound video object segmentation (SUVOS) and offers insights into future developments in this area
Pattern classification approaches for breast cancer identification via MRI: stateâofâtheâart and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current stateâofâtheâart computerâaided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multiâparametric
computerâaided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semiâsupervised deep learning and selfâsupervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a highâdimensional medical imaging analysis platform that is based on multiâtask
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCEâMRI. Since some of the approaches discussed are also based on
timeâlapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis
Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution
Objective. A phased or a curvilinear array produces ultrasound (US) images
with a sector field of view (FOV), which inherently exhibits spatially-varying
image resolution with inferior quality in the far zone and towards the two
sides azimuthally. Sector US images with improved spatial resolutions are
favorable for accurate quantitative analysis of large and dynamic organs, such
as the heart. Therefore, this study aims to translate US images with
spatially-varying resolution to ones with less spatially-varying resolution.
CycleGAN has been a prominent choice for unpaired medical image translation;
however, it neither guarantees structural consistency nor preserves
backscattering patterns between input and generated images for unpaired US
images. Approach. To circumvent this limitation, we propose a constrained
CycleGAN (CCycleGAN), which directly performs US image generation with unpaired
images acquired by different ultrasound array probes. In addition to
conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN
introduces an identical loss and a correlation coefficient loss based on
intrinsic US backscattered signal properties to constrain structural
consistency and backscattering patterns, respectively. Instead of
post-processed B-mode images, CCycleGAN uses envelope data directly obtained
from beamformed radio-frequency signals without any other non-linear
postprocessing. Main Results. In vitro phantom results demonstrate that
CCycleGAN successfully generates images with improved spatial resolution as
well as higher peak signal-to-noise ratio (PSNR) and structural similarity
(SSIM) compared with benchmarks. Significance. CCycleGAN-generated US images of
the in vivo human beating heart further facilitate higher quality heart wall
motion estimation than benchmarks-generated ones, particularly in deep regions
Explainable Semantic Medical Image Segmentation with Style
Semantic medical image segmentation using deep learning has recently achieved
high accuracy, making it appealing to clinical problems such as radiation
therapy. However, the lack of high-quality semantically labelled data remains a
challenge leading to model brittleness to small shifts to input data. Most
works require extra data for semi-supervised learning and lack the
interpretability of the boundaries of the training data distribution during
training, which is essential for model deployment in clinical practice. We
propose a fully supervised generative framework that can achieve generalisable
segmentation with only limited labelled data by simultaneously constructing an
explorable manifold during training. The proposed approach creates medical
image style paired with a segmentation task driven discriminator incorporating
end-to-end adversarial training. The discriminator is generalised to small
domain shifts as much as permissible by the training data, and the generator
automatically diversifies the training samples using a manifold of input
features learnt during segmentation. All the while, the discriminator guides
the manifold learning by supervising the semantic content and fine-grained
features separately during the image diversification. After training,
visualisation of the learnt manifold from the generator is available to
interpret the model limits. Experiments on a fully semantic, publicly available
pelvis dataset demonstrated that our method is more generalisable to shifts
than other state-of-the-art methods while being more explainable using an
explorable manifold